Ballast is a tool for balancing user load across SSH servers based on various criteria such as CPU load and system availability. It includes a load balancing client, a lightweight data server, and a data collection agent. Ballast is invoked as part of the SSH login process, so has access to the user name while making balancing decisions, which is not available in traditional load balancing approaches. This gives Ballast the unique ability to perform user-specific load balancing. This paper presents the Ballast architecture and examines the benefits of involving user-specific criteria in the balancing process. Two approaches for utilizing user information based on prediction and dynamic load metrics are analyzed using trace-based simulation and are found to have significant benefits when combined.

Grid computing promises gains in effective computational power, resource utilization, and resource accessibility, but in order to achieve these gains, organizations must deploy grid middleware that, in most cases, does not adhere to fundamental security principles. This paper introduces a new lightweight grid middleware called Mesh, which is based on the addition of a single sign-on capability to the built-in public key authentication mechanism of SSH using system call interposition. The initial Mesh implementation is compatible with approximately 90% of the world's SSH servers and any SSH client that supports public key authentication. Resources maybe added to a Mesh-based grid in a matter of minutes using just five small files and two environment variable settings. Mesh adheres to fundamental security principles and was designed to be compatible with strong security mechanisms including two-factor authentication, SSH bastions, and restrictive firewalls. Mesh uses a remote command model, which is based on the syntax and commands already understood by users, thus requires no additional knowledge to utilize effectively. Several existing services have been integrated with Mesh to provide resource discovery and query, high performance file transfer, and job management.

Grid computing aims to connect large numbers of geographically and organizationally distributed resources to increase computational power resource utilization, and resource accessibility. In order to effectively utilize Grids, users need to be connected to the best available resources at any given time. As Grids are in constant flux, users cannot be expected to keep up with the configuration and status of the Grid, thus they must be provided with automatic resource brokering for selecting and ranking resources meeting constraints and preferences they specify. This work presents a new OGSI-compliant resource selection and ranking framework called Surfer that has been implemented as part of NASA's Information Power Grid (IPG) project. Surfer is highly extensible and may be integrated into any Grid environment by adding information providers knowledgeable about that environment. Surfer invisibly and seamlessly correlates results from different providers into a single unified view seen by the user.

In this paper, we address the issues of resource management and fault tolerance in Grids. In Grids, the state of the selected resources for job execution is a primary factor that determines the computing performance. Specifically, we propose a resource manager for optimal resource selection. The resource manager automatically selects the optimal resources among candidate resources using a genetic algorithm. Typically, the probability of failure is higher in Grid computing than in a traditional parallel computing and the failure of resources affects job execution fatally. Therefore, a fault tolerance service is essential in computational Grids and Grid services are often expected to meet some minimum levels of quality of service (QoS) for desirable operation. To address this issue, we also propose fault tolerance service to satisfy QoS requirements. We extend the definition of failures, such as process failure, processor failure, and network failure, and design the fault detector and fault manager. The simulation results indicate that our approaches are promising in that (1) our resource manager finds the optimal set of resources that guarantees the optimal performance; (2) the fault detector detects the occurrence of resource failures; and (3) the fault manager guarantees that the submitted jobs complete and improves the performance of job execution due to job migration even if some failures happen.

This paper presents three sets of classification schemes for processes, properties, and transitions that can be used to assist in the analysis of real-time systems. These classification schemes are discussed in the context of ASTRAL, which is a formal specification language for real-time systems. Eight testbed systems were specified in ASTRAL, and their proofs were performed to determine proof patterns that occur most often. The specifications were then examined in an attempt to derive specific characteristics that could be used to statically identify each pattern within a specification. Once the classifications were obtained, they were then used to provide systematic guidance for analyzing real-time systems by directing the prover to the proof techniques most applicable to each proof pattern. This paper presents the set of classification schemes that were developed and discusses how they can be used to assist the proof process.